Bias
Also called AI bias
When a model's outputs systematically favor or disfavor certain groups — usually because its training data did.
Think of it like
A mirror that doesn't reflect everyone equally — it shows some people clearly and distorts others.
Example
A hiring model trained mostly on male resumes starts ranking women lower — not because it was told to, but because the data leaned that way.
How it actually works
Bias enters at every stage: data collection (who's represented), labeling (whose judgments), model design (what's optimized), and deployment (who's affected). It's not just a technical bug — it reflects and can amplify real-world inequities.
For product teams
Systematic unfairness in AI output — a legal, ethical, and reputational risk that needs active monitoring.
For engineers
Systematic skew from data/labeling/optimization; mitigate via diverse data, audits, disaggregated eval.
Related
- Rooted in the training data.
- Alignment — Alignment tries to reduce it.
- Red teaming — Red teaming is how you find it.
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